In general, marketing and sales do not use data to create and test hypotheses in the marketplace. Instead, they rely on intuition. New ideas occur to people within organizations all the time--but rarely are they born from the data and seldom are the marketplace results of these ideas captured to enhance the data.

By relying mainly on the gut-feel of marketers and salespeople, companies guarantee the perpetuation of shopworn beliefs. Some of these ideas are right and some are dead wrong. How do you know which are which? The answer is to let the facts be your guide. Gaining and using customer insight is a science not an art.... Companies seeking to improve their profitability will capture and systematically analyze data, create models, generate new ideas, run marketplace experiments, measure results, and adopt the things that work. Successful companies back up their brands and sales and marketing approaches by creating an infrastructure of data, facts, and analysis behind the scenes. They work to create processes, systems, and databases that ensure that every go-to-market idea and approach is grounded in measurable, provable business facts.

How Harrah's Used Customer Insight to Turn the Tables on the Gaming IndustryReturning to an example introduced earlier, casino company Harrah's Entertainment Inc. has had great success in targeting "low-rollers" in recent years. In fact, the approach was so successful that recent revenue growth and stock appreciation had far outpaced the gaming industry. By 2002, the company posted more than $4 billion in revenue, $235 million in net income and a streak of 16 straight quarters of "same-store" revenue growth. Harrah's now has 26 casinos in 13 states.

The results are so impressive that other casino operators are copying some of Harrah's more discernible methods. Wall Street analysts are also beginning to see Harrah's--long a dowdy also-ran in the flashy casino business--as gaining an edge on its rivals. Harrah's stock price has risen quickly as investors have received news of the marketing results. And the company's earnings have more than doubled in the past year.

Harrah's CEO explained how the company has dramatically improved customer loyalty, even during a challenging economy. For Harrah's, CRM consists of two key elements. First, it uses database marketing and decision-science-based analytical tools to ensure that operational and marketing decisions are based on fact rather than intuition. Second, it uses this insight, together with marketing experiments, to develop and implement service-delivery strategies that are finely tuned to customer needs.

Back in 1998, Harrah's decided that it wanted to change from an operations-driven company that viewed every casino as a stand-alone property to a marketing-driven company with a holistic view of its properties and customers. In effect, it wanted to move away from an OE-driven organization to one with a clear value proposition and competitive scope. This allowed Harrah's to focus its activities throughout the enterprise and meaningfully build its brand. In 1997, it had already implemented a loyalty program called Total Gold, which was a frequent-player program based on airline industry loyalty schemes. At first, the program was not highly differentiated within the gaming industry, varied across properties, and did not motivate customers to consolidate gaming at Harrah's properties. However, customer data derived from the program began the process of building the company's data mine. For example, Total Gold player cards recorded customer activity at various points of sale--including slot machines, restaurants, and shops. Soon, the database contained millions of transactions and valuable information about customer preferences and spending habits.

Once the data-mining process started in earnest, the first fact that jumped out was that Harrah's customers spent only 36 percent of their gaming dollars with the company. Also, they discovered that 26 percent of customers produced 82 percent of the revenues. Statistical analysis further revealed that the best customers were not the "high-rollers" so coveted by the rest of the industry. In fact, the best customers turned out to be slot-playing middle-aged folks or retired teachers, bankers, and doctors with time and discretionary income. They did not necessarily stay at a hotel, but often visited a casino just for the evening. Surveys of these customers told Harrah's that they visited casinos primarily because of the intense anticipation and excitement of gambling itself.

Given this insight, Harrah's decided to consolidate its strategy around these choice customers and focus branding, marketing, and the types of products and services being offered on meeting their needs. For example, Harrah's concentrated all of its advertising around the feeling of exuberance gambling produced for the segment. It developed quantitative models to predict lifetime value of these customers and used them to center marketing and service-delivery programs on increasing customer loyalty. It found that if a customer has a very happy experience with Harrah's they increased their spending on gambling at Harrah's by 24 percent a year. In contrast, unhappy experiences led to 10 percent declines. In an indication of success in capturing greater wallet-share, the programs dramatically increased the amount of cross-market (multiple property) play. This grew from 13 percent in 1997 to 23 percent in 2000.

Harrah's spent more time integrating data across properties, developing models, mining the data, and running marketing experiments. This, in turn, generated even more information on customer preferences and led to more insightful marketing and service delivery programs. Harrah's realized that the data, coupled with decision-science tools that allowed it to predict long-term value, enabled it to target marketing and service programs at individual player preferences. As Harrah's CEO said:

The further we get ahead and the more tests we run, the more we learn. The more we understand our customers, the more substantial the switching costs that we put in place, and the farther ahead we are of our competitors' efforts. That is why we are running as fast as we can.

Strategic focus, customer insight and resulting continuous optimization of its unique approach has propelled it to the primary position within its industry.

The 7 Dimensions of Customer InsightAs we saw with the Harrah's example, customer insight can come in many forms from many sources. It may relate to the age or gender of a customer, and their specific behavior before or after purchase. The information can be gathered electronically at the point of purchase, through face to face interactions, or emerge from analysis of a database containing customer-buying history. In this section we provide a framework to help categorize the various types of customer information that organizations typically seek to capture. We then lay out a process through which information can be gathered, analyzed and translated into action. We use seven broad dimensions to describe the customer information firms typically seek to capture, and below show example elements that companies tend to seek within each dimension:

What and how often customers buy

The products and services each customer is buying and has bought in the past.

The product configurations, additional features, service plans and other additional elements bought.

The frequency of purchases of each product.

The products or substitute products each customer buys or has bought from competitors.

Note: We have found most organizations do not spend enough time assessing "share-of-wallet" information. Usually, the first visibility they have into this is in market-share statistics gathered well after the fact.

Do they require special receipt, quality assurance, or delivery options.

What are their internal/personal circumstances

What are the customer's financial circumstances.

What are their strategic priorities.

How do customers put the product to use once purchased.

Do they perform activities in preparation for purchase or receipt of goods/service.

What other related activities or circumstances might impact buying decision/process or product use.

Note: For business-to-business transactions it is often very useful to map out the customer's value chain in order to best learn how products and services are truly put to use. This process creates opportunities to change the point at which the firm interacts with, or adds value to, the customer. For example, some firms have changed their relationship point with the customer by taking over inventory management or replenishment using pre-agreed rules.

What relevant external factors are in play

What are the competitive strengths and weaknesses of customer versus rivals.

Are there structural trends within the customer's industry (e.g. outsourcing, commoditization, etc.).

What are the key macro-economic factors influencing the customer.

What regulatory conditions impact the customer.

Are there any other key factors affecting the customer's circumstances.

What post sale interactions do customers require

What type and frequency of support does the customer require after purchase.

What information does the customer require after purchase.

Which channels does the customer prefer to interact through.

How often is the product returned or sent back in need of repair.

How often is repair or modification required due to specific customer circumstances.

It is clear that a tremendous amount of useful information can be captured about customers. Yet one of the most common mistakes made in building comprehensive data gathering processes is assembling too much data and organizing it poorly. When this occurs, the data becomes difficult to analyze and is accessible only by IT-skilled resources. However, when data gathering is implemented properly, it yields easily-understood information that can be put to use in ways that improve the effectiveness of both operations and strategy. Some of the concrete improvements that result from systematic collection of customer data are shown below:

Increased marketing effectiveness

Use of customer characteristics and buying patterns to segment the customer base into groups of similar types of customers allows the firm to craft tailored marketing approaches, sales and service plans for each group

Tailored service levels

Use of segment characteristics, and a detailed understanding of customer needs, to customize interactions and the types and levels of service delivered to customers.

Improved product development processes

Customer insight is fed back to improve product design and convey implicit information such as refined designs that eliminate common service complaints or recurring defects.

Understanding pricing, discounts and performance against volume purchase agreements can be tremendously revealing in most organizations. Most firms find realized price is well below expectations. Pricing rules and discipline can be improved based on better insight into individual and customer segment performance.More effective deployment of firm-wide resources

Use of segment value, needs, and performance data as the driver of resource deployment and focus throughout the firm. Resource deployment is rigid and political within most firms, meaning that at any given time too few resources are focused on the best opportunities. When customer-performance data is part of regular management reviews resource deployment usually improves.